可能性相关性反馈和语义网络用于目标提取
Possibilistic Pertinence Feedback and Semantic Networks for Goal's Extraction
摘要：
相关性反馈是一种技术，使用户能够交互地表达他的信息需求，通过修改他的原始查询公式与进一步的信息。通过明确确认由系统提取的一些指示对象和/或目标的相关性来提供该信息。显然，用户不能将对象和/或目标标记为相关的，直到一些被提取，所以第一次搜索必须由查询发起，并且初始查询规范必须足够好，以从语义网络中挑选出一些相关的对象和/或目标。在本文中，我们提出了一个简短的调查模糊和语义的知识提取方法。此类方法的目标是定义灵活的知识提取系统，能够处理提取过程固有的模糊性和不确定性。人们早就认识到，交互性提高了知识提取系统的有效性。新手用户的查询是最自然和交互式的通信媒介，最近的进展，在认识是有可能建立系统，与用户进行交互。然而，鉴于典型的新手用户的查询提交给知识提取系统，很容易想象，新手用户的查询中的目标识别错误的影响，必须严重破坏系统的有效性。本文报道的实验工作表明，在经典的知识提取技术的新手用户的查询处理的可能性理论的使用是更强大的比使用概率理论。此外，可能性和概率相关性反馈可以有效地提高新手用户的查询处理的有效性。
英文摘要：
Pertinence Feedback is a technique that enables a user to interactively express his information requirement by modifying his original query formulation with further information. This information is provided by explicitly confirming the pertinent of some indicating objects and/or goals extracted by the system. Obviously the user cannot mark objects and/or goals as pertinent until some are extracted, so the first search has to be initiated by a query and the initial query specification has to be good enough to pick out some pertinent objects and/or goals from the Semantic Network. In this paper we present a short survey of fuzzy and Semantic approaches to Knowledge Extraction. The goal of such approaches is to define flexible Knowledge Extraction Systems able to deal with the inherent vagueness and uncertainty of the Extraction process. It has long been recognised that interactivity improves the effectiveness of Knowledge Extraction systems. Novice user's queries are the most natural and interactive medium of communication and recent progress in recognition is making it possible to build systems that interact with the user. However, given the typical novice user's queries submitted to Knowledge Extraction Systems, it is easy to imagine that the effects of goal recognition errors in novice user's queries must be severely destructive on the system's effectiveness. The experimental work reported in this paper shows that the use of possibility theory in classical Knowledge Extraction techniques for novice user's query processing is more robust than the use of the probability theory. Moreover, both possibilistic and probabilistic pertinence feedback can be effectively employed to improve the effectiveness of novice user's query processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
